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International Symposium on Information Theory and Its Applications, ISITA

作者： Kazuki Ogawa Hiromu Takata Motohiko Isaka Dept. of Informatics Graduate School of Science and Technology Kwansei Gakuin University Sanda Japan Dept. of Computer Science School of Engineering Kwansei Gakuin University Sanda Japan

ISBN: (数字)9784885523410

ISBN: (纸本)9781665499071

Use of low-density generator matrix codes as codebook and encoding with message-passing algorithm is an efficient approach for lossy coding of the i.i.d. binary source. We propose lossy source encoder which is trained on the associated unfolded Tanner graph for improving the rate-distortion performance and/or reducing computational cost.

关键词： Codes Source coding Rate-distortion Generators Computational efficiency

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Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization

arXiv

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arXiv 2023年

作者： Balaskas, Konstantinos Karatzas, Andreas Sad, Christos Siozios, Kostas Anagnostopoulos, Iraklis Zervakis, Georgios Henkel, Jörg The Department of Physics Aristotle University of Thessaloniki Thessaloniki54124 Greece Karlsruhe Institute of Technology Karlsruhe76131 Germany The School of Electrical Computer and Biomedical Engineering Southern Illinois University Carbondale CarbondaleIL62901 United States The Dept. of Computer Engineering & Informatics University of Patras Patras26504 Greece

Deep Neural Networks (DNNs) have shown significant advantages in a wide variety of domains. However, DNNs are becoming computationally intensive and energy hungry at an exponential pace, while at the same time, there is a vast demand for running sophisticated DNN-based services on resource constrained embedded devices. In this paper, we target energy-efficient inference on embedded DNN accelerators. To that end, we propose an automated framework to compress DNNs in a hardware-aware manner by jointly employing pruning and quantization. We explore, for the first time, per-layer fine- and coarse-grained pruning, in the same DNN architecture, in addition to low bit-width mixed-precision quantization for weights and activations. Reinforcement Learning (RL) is used to explore the associated design space and identify the pruning-quantization configuration so that the energy consumption is minimized whilst the prediction accuracy loss is retained at acceptable levels. Using our novel composite RL agent we are able to extract energy-efficient solutions without requiring retraining and/or fine tuning. Our extensive experimental evaluation over widely used DNNs and the CIFAR-10/100 and ImageNet datasets demonstrates that our framework achieves 39% average energy reduction for 1.7% average accuracy loss and outperforms significantly the state-of-the-art approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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A Method for Predicting Cardiovascular Disorder using Machine Learning Techniques

A Method for Predicting Cardiovascular Disorder using Machin...

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Recent Advances in Science and engineering technology (ICRASET), International Conference on

作者： Ramakrishna Hegde D R Pavithra S Shivashankara G Prasanna Kumar S M Soumyasri S Nagashree Computer Science and Engg. Vidyavardaka College of Engineering Affiliated to Visvesvaraya Technological University Belagavi India Electronics & Communication Engg. Sri Jayachamarajendra College of Engineering JSS Science and Technology University Mysuru India Computer Science and Engg. KR Pete Krishna Government Engineering College Krishnarajapet India Information Science & Engg. The National Institute of Engineering Mysore India Dept. of Computer Applications Vidya Vikas Inst. of Engg. and Tech Affiliated to Visvesvaraya Technological University Belagavi India Electronics and communication Engg Vidya Vikas Institute of Engineering and Technology Mysuru India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

Drastic increase of cardiovascular disease has led to a lot of adult’s death. As per “News 18” very year, 12 million young people in India die from heart disease. In most of these cases people experience cardiac arrest, some of which are normal. First, families of patients are vulnerable because it takes only a few minutes for a person to die of a heart attack, and it is difficult to get medical help in time. Second, most victims are under the age of 45. According to Dr. Maninder Sandhu, cardiologist and cardiologist at Artemis Hospital Gurgaon, 30 percent of people who suffer from heart disease are under the age of 45. This paper proposes a method that maybe used by laboratory technicians, doctors and users to understand whether there are any chances of having cardiovascular disease or not. The proposed system takes live data of patients and their lifestyle attributes these data are processed by the trained model to produce the output. In this model we haveused random forest classifier, linear regression, $\mathbf{k}$-neighbors classifier & x gradient boost classifier. This model has attained maximum accuracy and least type one error in “X- Gradient Boost”. We have concentrated more on type one error. The developed system shows whether the patient has any chances of cardiovascular disease or not.

关键词： Heart Accuracy Linear regression Cardiac arrest Predictive models Nearest neighbor methods Feature extraction Prediction algorithms Classification algorithms Random forests

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MRIGAT: Many-to-many Recommendation using Interaction based Knowledge Graph Attention 22

MRIGAT: Many-to-many Recommendation using Interaction based ...

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3rd International Symposium on Artificial Intelligence for Medical Sciences, ISAIMS 2022

作者： Gao, Feng Yuan, Kai Gu, Jinguang Liu, Yun Department of Computer Science and Technology Wuhan University of Science and Technology China The Key Laboratory of Rich-Media Knowledge Organization Service of Digital Publishing Content Institute of Scientific and Technical Information of China China Guangxi Eco-engineering Vocational & Technical College China 1st Affiliated Hospital Dept. of Medical Informatics School of Biomedical Engineering & Informatic Nanjing Medical University China

ISBN: (纸本)9781450398442

Recommendation systems combining Graph Neural Networks and Knowledge Graphs have been successfully applied in various domains. However, most existing approaches only consider one-to-one or one-to-many user-item interactions and cannot cater to many-to-many recommendation scenarios, such as providing prescriptions based on clinical diagnosis and medical history. Providing such a recommendation requires considering the implicit interaction within input user features as well as output candidates. In this paper, we propose a two-stage knowledge graph attention aggregation mechanism that helps recommend drug combinations for a patient based on his conditions. First, a disease-drug interaction graph is constructed and integrated with the medical domain knowledge, forming a collaborative knowledge graph. Secondly, an intra-feature attention aggregation is performed to obtain the representations of diseases and drugs based on drug-drug and disease-disease interactions, respectively. Thirdly, an inter-feature attention aggregation is performed using the disease-drug interaction to better represent a user's condition. Finally, the user's condition representation is concatenated with other user features to generate the final user representation for the prescription recommendation. Experiments with realistic datasets show that our approach can outperform existing recommendation systems by 4.3% and 6.1% in precision and recall, respectively. © 2022 ACM.

关键词： Knowledge graph

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An Ultra-Low Power (86 nW) Low-Voltage (0.6 V) Self-Biased inst.umentation Amplifier for Bio-Medical Applications

An Ultra-Low Power (86 nW) Low-Voltage (0.6 V) Self-Biased I...

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IEEE Electron Devices Kolkata Conference (EDKCON)

作者： Koyel Mukherjee Soumya Pandit Rajat Kumar Pal Inst. of Radio Physics and Electronics University of Calcutta Kolkata India Dept. of Computer Science and Engineering University of Calcutta Kolkata India

ISBN: (纸本)9781665472067

In this article, an operational transconductance amplifier (OTA)-based self-biased, ultra-low power inst.umentation amplifier is proposed. The design is performed in SCL 0.18 μm technology under 0.6 V power supply. The total current drawn from the supply is 144 nA, resulting in ultra-low power consumption of 86 nW. The entire circuit is self-biased by an in-built current reference circuit. The inst.umentation amplifier produces a fixed gain of 61 dB with unity gain bandwidth of 4.1 KHz and input RMS noise voltage of 2.01 μV rms . The amplifier is aimed for bio-medical applications and is capable of amplifying a signal as low as 0.5 μV with an ultra-low frequency of 1 Hz also, serving the range of even the weakest possible bio-potential signal quite efficiently.

关键词： Performance evaluation Low voltage Power demand inst.uments Voltage Bandwidth Low-power electronics

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Development of a Calibration-Free Brain-computer Interface Utilizing Common Spatial Patterns and Artificial Neural Networks for EEG Signal Analysis

Development of a Calibration-Free Brain-Computer Interface U...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Korhan Cengiz Shreyas J Udayaprasad P K Gururaj H L Dilip Kumar S M College of Information Technology University of Fujairah UAE Faculty of Organization and Informatics University of Zagreb Croatia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal Karnataka India Department of Artificial Intelligence & Machine Learning Global Academy of Technology Bengaluru India Dept. of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore India

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

A brain computer interface (BCI) system uses a technique known calibration, that takes 20 to 30 minutes to accomplish. For the objective of creating a reliable decoder, the calibration process is challenging and expensive. In order to address the drawbacks of the current system, a spectral-spatial technique has been suggested. The motor imagery (MI) data set, comprising 15 electroencephalography (EEG) signals and fourteen test subjects, is taken into consideration. The two modules are designed to extract characteristics and process data. An artificial neural network (ANN) is used to independently train and test the suggested spectral-spatial algorithm. Based on it, a variety of machine learning techniques, including random forest (RF), neural networks (NN), and XGboost, are used to classify the data, that is then sent to the hidden layer (Lth layer). The obtained results indicates 2% of improvement in comparison with existing methodology.

关键词： Training Machine learning algorithms Artificial neural networks Electroencephalography Brain-computer interfaces Calibration Reliability

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Minimax Flow over Acyclic Networks: Distributed Algorithms and Microgrid Application

arXiv

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arXiv 2022年

作者： Coraggio, Marco Jafarpour, Saber Bullo, Francesco di Bernardo, Mario School for Advanced Studies Italy The Dept. of Electrical and Computer Engineering Georgia Inst. of Technology United States The Dept. of Mechanical Engineering Univ. of California Santa Barbara United States The Dept. of Information Technology and Electrical Engineering Univ. of Naples Federico II Italy The SSM Italy

Given a flow network with variable suppliers and fixed consumers, the minimax flow problem consists in minimizing the maximum flow between nodes, subject to flow conservation and capacity constraints. We solve this problem over acyclic graphs in a distributed manner by showing that it can be recast as a consensus problem between the maximum downstream flows, which we define here for the first time. Additionally, we present a distributed algorithm to estimate these quantities. Finally, exploiting our theoretical results, we design an online distributed controller to prevent overcurrent in microgrids consisting of loads and droop-controlled inverters. Our results are validated numerically on the CIGRE benchmark microgrid. © 2022, CC BY-NC-ND.

关键词： Flow graphs

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Towards Stagewise and Energy-Accuracy-balanced Client Selection and Resource Allocation over Dynamic Federated Learning Networks

Towards Stagewise and Energy-Accuracy-balanced Client Select...

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IEEE International Conference on Communications in China (ICCC)

作者： Minghong Wu Minghui Liwang Yuhan Su Yuliang Tang Zhenzhen Jiao Xianbin Wang School of Informatics Xiamen University Xiamen China College of Electronic and Information Engineering Tongji University Shanghai China School of Electronic Science and Engineering Xiamen University Xiamen China iF-Labs Beijing Teleinfo Technology Co. Ltd. CAICT China Dept. of Electrical and Computer Engineering Western University Ontario Canada

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Federated Learning (FL) represents a popular distributed learning architecture that facilitates data privacy by enabling clients (e.g., mobile devices) to train a FL model collaboratively without data sharing. Existing efforts mainly focus on accuracy, delay and energy consumption over a rather stable network with certain clients, with less emphasis on the impact of frequent decision-making processes during model training. Inspired by this, this paper considers a dynamic FL network with uncertain participation of clients, while we jointly optimize client selection and communication resource allocation to achieve a balance between energy cost and FL model accuracy, as proved to be NP-hard. To facilitate timely model training, we propose a prediction-based two-stage asynchronous programming mechanism, which decouples the problem in two subproblems, corresponding to two stages. In particular, the former stage determines some long-term clients which are more stable to join in prior to practical model training process, by estimating the online probability of clients. Then, the latter stage can be implemented by involving some temporary clients as backups when long-term ones are not able to show up. Such a well-designed mechanism offers a unique veiw on FL, while enabling a responsive and cost-effective decision-making process. Comprehensive simulations regarding both IID and non-IID data distributions on MNIST and CIFAR-10 datasets can prove our commendable performance on time efficiency, energy cost and accuracy.

关键词： Training Accuracy Costs Federated learning Decision making Programming Dynamic scheduling

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Goal-Aware RSS for Complex Scenarios via Program Logic

Goal-Aware RSS for Complex Scenarios via Program Logic

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IEEE Symposium on Intelligent Vehicle

作者： Ichiro Hasuo Clovis Eberhart James Haydon Jérémy Dubut Rose Bohrer Tsutomu Kobayashi Sasinee Pruekprasert Xiao-Yi Zhang Erik André Pallas Akihisa Yamada Kohei Suenaga Fuyuki Ishikawa Kenji Kamijo Yoshiyuki Shinya Takamasa Suetomi National Institute of Informatics (NII) Tokyo Japan SOKENDAI (The Graduate University for Advanced Studies) Japan Japanese-French Laboratory for Informatics (IRL 3527) Tokyo Japan Dept. Computer Science Worcester Polytechnic Institute Worcester MA USA Inst. Software & Systems Engineering University of Augsburg Augsburg Germany Cyber Physical Security Research Center AIST Tokyo Japan Graduate School of Informatics Kyoto University Kyoto Japan Mazda Motor Corporation Fuchu Japan

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

We introduce a goal-aware extension of responsibility-sensitive safety (RSS), a recent methodology for rule-based safety guarantee for automated driving systems (ADS). Making RSS rules guarantee goal achievement—in addition to collision avoidance as in the original RSS—requires complex planning over long sequences of manoeuvres. To deal with the complexity, we introduce a compositional reasoning framework based on program logic, in which one can systematically develop RSS rules for smaller subscenarios and combine them to obtain RSS rules for bigger scenarios. As the basis of the framework, we introduce a program logic dFHL that accommodates continuous dynamics and safety conditions. Our framework presents a dFHL-based workflow for deriving goal-aware RSS rules; we discuss its software support, too. We conducted experimental evaluation using RSS rules in a safety architecture. Its results show that goal-aware RSS is indeed effective in realising both collision avoidance and goal achievement.

关键词： Intelligent vehicles computer architecture Software Cognition Safety Planning Complexity theory

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Heterogeneous CFET: A Pathway to CMOS Performance Balance engineering

Heterogeneous CFET: A Pathway to CMOS Performance Balance En...

引用

IEEE Silicon Nanoelectronics Workshop (SNW)

作者： Xin-Ren Yu Wen-Hsiang Lu Shu-Wei Chang Wen-Hsin Chang Darsen D. Lu Yao-Jen Lee Tatsuro Maeda Wen-Fa Wu Yi-Ming Li Yeong-Her Wang Inst. of Microelectronics Natl. Cheng Kung University Tainan Taiwan Dept. of Electrical Engineering Natl. Cheng Kung University Tainan Taiwan National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Japan Inst. of Pioneer Semiconductor Innovation National Yang Ming Chiao Tung University Hsinchu Taiwan Taiwan Semiconductor Research Inst. (TSRI) Hsinchu Taiwan College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

The CFETs can provide high-performance characteristics with significant area reduction for Å technology nodes. However, mobilities between n/p FETs need to be considered for the performance balance of CMOS. This ... 详细信息

ISBN: (数字)9798350391633

ISBN: (纸本)9798350391640

关键词： Wafer bonding Conferences Field effect transistors Silicon Nanoelectronics

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